how many training samples are required for knn classifier? (minimum number of samples)

for knn classifier to be used what is the minimum required number of training data or samples needed?

Answer 1

[Krishna]

Hi Pranjal,

I understand that you want to know how to choose the number of samples for training KNN classifiers.

There is no strict minimum requirement for the number of training samples needed for a K-Nearest Neighbors (KNN) classifier, but certain guidelines help ensure better performance.

Ideally, you should have at least K+1 samples per class to allow the classifier to effectively compare and classify new data points.

In practice, it is recommended to have 10 to 30 times the number of features per class for reliable results.

For high-dimensional data, a larger dataset is necessary to avoid sparsity and maintain generalization.

Since KNN relies on distance calculations between data points, having a sparse training set can lead to poor classification accuracy. More data typically helps the model cover the feature space better and improve decision-making.

Hope this helps.